Experiment with Random Difficulty (results based on the confidence scale)
## Nombre de participants à l'expérimentation : 58## Nombre de participants se déclarant comme joueurs : 29## Nombre de femmes se déclarant comme joueuses : 3## Age médian des joueurs : 15Removing Outliers based on BET
(pas nécessaire pour la mesure basée sur l’échelle de confiance)
{r removing.outliers.setup.bet, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SETUP # #------------------------------------------------------ # # DTM <- DTAll[which(DTAll$nom_du_jeu=="Motrice"),] # DTL <- DTAll[which(DTAll$nom_du_jeu=="Logique2"),] # DTS <- DTAll[which(DTAll$nom_du_jeu=="Sensoriel"),] # # # get.outliers <- function(DTDescMLoc,DTDescSLoc,DTDescLLoc){ # outliersM <- boxplot.stats(DTDescMLoc$var)$out # outliersS <- boxplot.stats(DTDescSLoc$var)$out # outliersL <- boxplot.stats(DTDescLLoc$var)$out # # outliers = data.table(type=character(0),id=character(0)) # setkey(outliers,id) # if(length(outliersM) > 0) # outliers = merge(outliers,data.table(id=DTDescMLoc[var %in% outliersM]$IDjoueur,type="Moteur"),by=c("id","type"),all=TRUE) # if(length(outliersS) > 0) # outliers = merge(outliers,data.table(id=DTDescSLoc[var %in% outliersS]$IDjoueur,type="Sensoriel"),by=c("id","type"),all=TRUE) # if(length(outliersL) > 0) # outliers = merge(outliers,data.table(id=DTDescLLoc[var %in% outliersL]$IDjoueur,type="Logique"),by=c("id","type"),all=TRUE) # # return(outliers) # } # # plot.outliers <- function(DT,title){ # p <- ggplot(DT, # aes(type,var)) + # xlab("Difficulty Type") + # ylab(title) # p <- p + geom_boxplot() + geom_point(shape=1) # print(p) # } #
{r detect.outliers.bet.sd, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS BET STD DEV # #------------------------------------------------------ # DTDescM = DTM[,.(type="Moteur",var=sd(miseNorm)),by=IDjoueur] # DTDescS = DTS[,.(type="Sensoriel",var=sd(miseNorm)),by=IDjoueur] # DTDescL = DTL[,.(type="Logique",var=sd(miseNorm)),by=IDjoueur] # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "Bet Standard Dev"); # # outliers = get.outliers(DTDescM,DTDescS,DTDescL) # print(paste("Outliers BET STANDARD DEVIATION:",toString(outliers$id))) # # DTM[IDjoueur %in% unlist(outliers[type=="Moteur"]$id) ,{plot.diff.curve(.SD,"Outlier Bet Sd Motor Task");NULL},by=.(IDjoueur)] # DTS[IDjoueur %in% unlist(outliers[type=="Sensoriel"]$id) ,{plot.diff.curve(.SD,"Outlier Bet Sd Sensory Task");NULL},by=.(IDjoueur)] # DTL[IDjoueur %in% unlist(outliers[type=="Logique"]$id) ,{plot.diff.curve(.SD,"Outlier Bet Sd Logical Task");NULL},by=.(IDjoueur)] #
{r detect.outliers.win.sum.bet, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SUM OF WINS # #------------------------------------------------------ # # Difficulty : win sum # # # DTDescM = DTM[,.(type="Moteur",var=sum(gagnant)),by=IDjoueur] # # DTDescS = DTS[,.(type="Sensoriel",var=sum(gagnant)),by=IDjoueur] # # DTDescL = DTL[,.(type="Logique",var=sum(gagnant)),by=IDjoueur] # # # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "Win Sum"); # # # # outliersLoc = get.outliers(DTDescM,DTDescS,DTDescL) # # outliers = merge(outliers,outliersLoc,by=c("id","type"),all=TRUE) # # print(paste("Outliers :",toString(outliersLoc$id))) # # # # DTM[IDjoueur %in% unlist(outliersLoc[type=="Moteur"]$id) ,{plot.diff.curve(.SD,"Outlier Win Sum Motor Task");NULL},by=.(IDjoueur)] # # DTS[IDjoueur %in% unlist(outliersLoc[type=="Sensoriel"]$id) ,{plot.diff.curve(.SD,"Outlier Win Sum Sensory Task");NULL},by=.(IDjoueur)] # # DTL[IDjoueur %in% unlist(outliersLoc[type=="Logique"]$id) ,{plot.diff.curve(.SD,"Outlier Win Sum Logical Task");NULL},by=.(IDjoueur)] # #
{r detect.outliers.sheeps.saved.bet, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SAVED SHEEPS # #------------------------------------------------------ # # Difficulty and strategy = saved sheeps # DTDescM = DTM[,.(type="Moteur",var=max(moutons_sauves)),by=IDjoueur] # DTDescS = DTS[,.(type="Sensoriel",var=max(moutons_sauves)),by=IDjoueur] # DTDescL = DTL[,.(type="Logique",var=max(moutons_sauves)),by=IDjoueur] # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "Saved sheeps"); # # outliersLoc = get.outliers(DTDescM,DTDescS,DTDescL) # outliers = merge(outliers,outliersLoc,by=c("id","type"),all=TRUE) # print(paste("Outliers BET SAVED SHEEPS:",toString(outliersLoc$id))) # # DTM[IDjoueur %in% unlist(outliersLoc[type=="Moteur"]$id) ,{plot.diff.curve(.SD,"Outlier Score Motor Task");NULL},by=.(IDjoueur)] # DTS[IDjoueur %in% unlist(outliersLoc[type=="Sensoriel"]$id) ,{plot.diff.curve(.SD,"Outlier Score Sensory Task");NULL},by=.(IDjoueur)] # DTL[IDjoueur %in% unlist(outliersLoc[type=="Logique"]$id) ,{plot.diff.curve(.SD,"Outlier Score Logical Task");NULL},by=.(IDjoueur)] # #
{r detect.outliers.dda.exploit.bet, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS EXPLOIT DDA # #------------------------------------------------------ # # DDA Exploit : Win/Fail delta sum max # DTDescM = DTM[,.(type="Moteur",var=max(cumulDeltaMise)),by=IDjoueur] # DTDescS = DTS[,.(type="Sensoriel",var=max(cumulDeltaMise)),by=IDjoueur] # DTDescL = DTL[,.(type="Logique",var=max(cumulDeltaMise)),by=IDjoueur] # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "Win/Fail delta sum max"); # # outliersLoc = get.outliers(DTDescM,DTDescS,DTDescL) # outliers = merge(outliers,outliersLoc,by=c("id","type"),all=TRUE) # print(paste("Outliers BET EXPLOIT DDA:",toString(outliersLoc$id))) # # DTM[IDjoueur %in% unlist(outliersLoc[type=="Moteur"]$id) ,{plot.diff.curve(.SD,"Outlier Delta Bet Motor Task");NULL},by=.(IDjoueur)] # DTS[IDjoueur %in% unlist(outliersLoc[type=="Sensoriel"]$id) ,{plot.diff.curve(.SD,"Outlier Delta Bet Sensory Task");NULL},by=.(IDjoueur)] # DTL[IDjoueur %in% unlist(outliersLoc[type=="Logique"]$id) ,{plot.diff.curve(.SD,"Outlier Delta Bet Logical Task");NULL},by=.(IDjoueur)] #
{r detect.outliers.summary.bet, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SUMMARY # #------------------------------------------------------ # print(paste("Total number of outliers: ",toString(nrow(unique(outliers,by="id"))))) # print(paste("Total number of outliers motor task: ",toString(nrow(unique(outliers[type=="Moteur"],by="id"))))) # print(paste("Total number of outliers perceptive task: ",toString(nrow(unique(outliers[type=="Logique"],by="id"))))) # print(paste("Total number of outliers logical task: ",toString(nrow(unique(outliers[type=="Sensoriel"],by="id"))))) #
{r remove.outliers.bet, echo=FALSE} # #------------------------------------------------------ # # REMOVING OUTLIERS FROM TABLES # #------------------------------------------------------ # # removing all outliers # DTM <- DTM[!IDjoueur %in% unlist(outliers[type=="Moteur"]$id)] # DTS <- DTS[!IDjoueur %in% unlist(outliers[type=="Sensoriel"]$id)] # DTL <- DTL[!IDjoueur %in% unlist(outliers[type=="Logique"]$id)] # DTAll <- data.table() # DTAll <- rbind(DTAll,DTL) # DTAll <- rbind(DTAll,DTM) # DTAll <- rbind(DTAll,DTS) #
Removing Outliers based on CONFIDENCE SCALE
## [1] "Outliers CS STANDARD DEVIATION: 9b3ph38yc, 9b3ph38yc, a6dfu5ljd, a6dfu5ljd, bzrji9dqz, dyg7cga2o, dyg7cga2o, ejodnl05c, kctu3te1y, tmxmxmwhi, zp9bc59o5, zv35u39vc"## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Outliers CS NULL: 9b3ph38yc, 9b3ph38yc, 9b3ph38yc, a6dfu5ljd, a6dfu5ljd, a6dfu5ljd, bzrji9dqz, bzrji9dqz, dyg7cga2o, dyg7cga2o, dyg7cga2o, e58u3sinl, kctu3te1y, kctu3te1y, m4ye7uz5h, qzh5zi9e8, tmxmxmwhi, tmxmxmwhi, urgv6o806, zp9bc59o5, zp9bc59o5, zv35u39vc"
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Outliers : "## Empty data.table (0 rows) of 1 col: IDjoueur## Empty data.table (0 rows) of 1 col: IDjoueur## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Outliers CS SAVED SHEEPS: "## Empty data.table (0 rows) of 1 col: IDjoueur## Empty data.table (0 rows) of 1 col: IDjoueur## Empty data.table (0 rows) of 1 col: IDjoueur## [1] "Total number of outliers: 12"## [1] "Total number of outliers motor task: 11"## [1] "Total number of outliers perceptive task: 5"## [1] "Total number of outliers logical task: 6"Modeling difficulties
Modeling objective difficulty for motor task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 1669.2 1690.0 -830.6 1661.2 1359
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8343 -0.7720 0.3062 0.7571 2.7501
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.4686 0.6846
## Number of obs: 1363, groups: IDjoueur, 47
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.9982 0.1974 -5.057 4.27e-07 ***
## difficulty 2.8413 0.2301 12.346 < 2e-16 ***
## timeNorm -0.5530 0.2179 -2.538 0.0112 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.549
## timeNorm -0.577 -0.022## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.##
## Logique2 Motrice Sensoriel
## 0 1363 0
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-0.96344
## 1st Qu.:-0.37670
## Median :-0.08364
## Mean :-0.00173
## 3rd Qu.: 0.21652
## Max. : 1.57591
## [1] "Intercept: -0.998 4.3e-07 ***"
## [1] "Difficulty: 2.84 5.1e-35 ***"
## [1] "Time: -0.553 0.011 *"
## [1] "R2 fixed: 0.16"
## [1] "R2 mixed: 0.26"
## [1] "Cross Val: 0.69"
## [1] "AIC: 1700"## 0% 25% 50% 75% 100%
## -1.57590870 -0.21652213 0.08364306 0.37669604 0.96343672
## 0% 25% 50% 75% 100%
## -1.57590870 -0.21652213 0.08364306 0.37669604 0.96343672
## `geom_smooth()` using method = 'gam'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
Modeling objective difficulty for sensory task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 1173.9 1195.1 -582.9 1165.9 1504
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.2906 -0.3676 0.1154 0.3469 6.2131
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.7411 0.8609
## Number of obs: 1508, groups: IDjoueur, 52
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.1668 0.2640 -11.996 <2e-16 ***
## difficulty 8.1536 0.4159 19.606 <2e-16 ***
## timeNorm -0.4920 0.2782 -1.768 0.077 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.633
## timeNorm -0.505 -0.080## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : Model failed to converge with max|grad| = 0.0610209 (tol =
## 0.001, component 1)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?## The result is correct only if all data used by the model has not changed since model was fitted.## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.0610209 (tol = 0.001, component 1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?## The result is correct only if all data used by the model has not changed since model was fitted.##
## Logique2 Motrice Sensoriel
## 0 0 1508
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-1.677712
## 1st Qu.:-0.448501
## Median : 0.077197
## Mean :-0.001156
## 3rd Qu.: 0.407249
## Max. : 1.510666
## [1] "Intercept: -3.17 3.7e-33 ***"
## [1] "Difficulty: 8.15 1.4e-85 ***"
## [1] "Time: -0.492 0.077 ."
## [1] "R2 fixed: 0.3"
## [1] "R2 mixed: 0.46"
## [1] "Cross Val: 0.82"
## [1] "AIC: 1200"## 0% 25% 50% 75% 100%
## -1.51066561 -0.40724859 -0.07719681 0.44850104 1.67771216
## 0% 25% 50% 75% 100%
## -1.51066561 -0.40724859 -0.07719681 0.44850104 1.67771216
## `geom_smooth()` using method = 'gam'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
Modeling objective difficulty for logical task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 1444.5 1465.8 -718.2 1436.5 1533
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.0357 -0.4980 -0.1017 0.5004 5.0622
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 1.57 1.253
## Number of obs: 1537, groups: IDjoueur, 53
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.9054 0.2628 -7.251 4.14e-13 ***
## difficulty 5.7562 0.3198 18.001 < 2e-16 ***
## timeNorm -1.9355 0.2564 -7.550 4.35e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.497
## timeNorm -0.376 -0.233## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.##
## Logique2 Motrice Sensoriel
## 1537 0 0
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-1.8051717
## 1st Qu.:-0.7513212
## Median :-0.2064150
## Mean :-0.0003176
## 3rd Qu.: 0.7228639
## Max. : 3.1492300
## [1] "Intercept: -1.91 4.1e-13 ***"
## [1] "Difficulty: 5.76 1.9e-72 ***"
## [1] "Time: -1.94 4.4e-14 ***"
## [1] "R2 fixed: 0.38"
## [1] "R2 mixed: 0.58"
## [1] "Cross Val: 0.79"
## [1] "AIC: 1400"## 0% 25% 50% 75% 100%
## -3.1492300 -0.7228639 0.2064150 0.7513212 1.8051717
## 0% 25% 50% 75% 100%
## -3.1492300 -0.7228639 0.2064150 0.7513212 1.8051717
## `geom_smooth()` using method = 'gam'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
Influence of Player Profiles
Player profiles
Influence of Player Profiles
Objective level and player profile
Playing video games in general and level for each task
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.37495, p-value = 0.7077
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.04294701
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.91744, p-value = 0.3589
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.1000199
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.12965, p-value = 0.8968
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.01388433Playing board games in general and level for each task
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.99227, p-value = 0.3211
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1118
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.21922, p-value = 0.8265
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.02354007
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.6523, p-value = 0.5142
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.06919576Self efficacy and level for each task
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 23 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.24953, p-value = 0.8029
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.03718731## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 24 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.4833, p-value = 0.01302
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.3393258
##
## [1] "self.eff.on.level.s 0.34 0.013 *"## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 27 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.46598, p-value = 0.6412
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.06648267Risk aversion and level for each task
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.3418, p-value = 0.1797
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1465938
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.9118, p-value = 0.0559
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1966642
##
## [1] "risk.av.on.level.s 0.2 0.056 ."
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.3781, p-value = 0.1682
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1404273Age and level for each task
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 1 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.3062, p-value = 0.1915
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.1372263## Warning: Removed 1 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.9837, p-value = 0.04728
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1984774
##
## [1] "age.on.level.s 0.2 0.047 *"## Warning: Removed 1 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.1451, p-value = 0.2522
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1130316Sex and level for each task
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -2.0369, p-value = 0.04166
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2478106
##
## [1] "sexe.on.level.m -0.25 0.042 *"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.068275, p-value = 0.9456
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.007880754
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.38949, p-value = 0.6969
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.04451521
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 163, p-value = 0.04192
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.73654416 -0.04033621
## sample estimates:
## difference in location
## -0.3800085
##
## [1] "sexe.on.level.m.2 -0.38 0.042 * mean(A): 0.15 mean(B): -0.27"
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 294, p-value = 0.9538
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.4708587 0.5066973
## sample estimates:
## difference in location
## -0.02056307
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 302, p-value = 0.7064
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.7753238 0.5708569
## sample estimates:
## difference in location
## -0.06017729CONFIDENCE SCALE APPROACH
For Bet approach, see the other file.
Influence of Objective difficulty on Subjective Difficulty
All tasks
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.079 44 0.0014 **
## 2: 0.09375 0.120 54 6.5e-05 ***
## 3: 0.15625 0.110 55 0.00029 ***
## 4: 0.21875 0.120 53 1e-04 ***
## 5: 0.28125 0.100 53 0.00018 ***
## 6: 0.34375 0.094 50 0.00011 ***
## 7: 0.40625 0.074 53 0.033 *
## 8: 0.46875 0.011 53 0.63 :(
## 9: 0.53125 -0.014 50 0.6 :(
## 10: 0.59375 -0.058 54 0.0054 **
## 11: 0.65625 -0.078 52 0.00079 ***
## 12: 0.71875 -0.110 54 3.5e-05 ***
## 13: 0.78125 -0.160 53 3.2e-07 ***
## 14: 0.84375 -0.220 52 1.2e-08 ***
## 15: 0.90625 -0.230 55 3.8e-10 ***
## 16: 0.96875 -0.170 55 1.3e-09 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 44 0.0014 **
## 2: 54 6.5e-05 ***
## 3: 55 0.00029 ***
## 4: 53 1e-04 ***
## 5: 53 0.00018 ***
## 6: 50 0.00011 ***
## 7: 53 0.033 *
## 8: 53 0.63 :(
## 9: 50 0.6 :(
## 10: 54 0.0054 **
## 11: 52 0.00079 ***
## 12: 54 3.5e-05 ***
## 13: 53 3.2e-07 ***
## 14: 52 1.2e-08 ***
## 15: 55 3.8e-10 ***
## 16: 55 1.3e-09 ***
## [1] 52.5
## [1] 2.73
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.089 19 0.085 .
## 2: 0.09375 0.056 22 0.38 :(
## 3: 0.15625 0.044 28 0.83 :(
## 4: 0.21875 0.038 31 0.26 :(
## 5: 0.28125 0.044 29 0.19 :(
## 6: 0.34375 0.069 30 0.048 *
## 7: 0.40625 0.054 30 0.11 :(
## 8: 0.46875 0.048 29 0.075 .
## 9: 0.53125 0.039 27 0.48 :(
## 10: 0.59375 -0.034 30 0.42 :(
## 11: 0.65625 -0.110 28 0.024 *
## 12: 0.71875 -0.190 27 0.00065 ***
## 13: 0.78125 -0.180 26 0.0029 **
## 14: 0.84375 -0.220 13 0.02 *
## 15: 0.90625 -0.280 13 0.0033 **
## 16: 0.96875 -0.024 11 0.45 :(
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 19 0.085 .
## 2: 22 0.38 :(
## 3: 28 0.83 :(
## 4: 31 0.26 :(
## 5: 29 0.19 :(
## 6: 30 0.048 *
## 7: 30 0.11 :(
## 8: 29 0.075 .
## 9: 27 0.48 :(
## 10: 30 0.42 :(
## 11: 28 0.024 *
## 12: 27 0.00065 ***
## 13: 26 0.0029 **
## 14: 13 0.02 *
## 15: 13 0.0033 **
## 16: 11 0.45 :(
## [1] 24.6
## [1] 6.81
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.0370 33 0.046 *
## 2: 0.09375 0.1400 37 0.00034 ***
## 3: 0.15625 0.1400 38 0.00058 ***
## 4: 0.21875 0.1800 38 0.00028 ***
## 5: 0.28125 0.1600 38 0.0021 **
## 6: 0.34375 0.0940 34 0.031 *
## 7: 0.40625 0.0520 38 0.32 :(
## 8: 0.46875 -0.0062 37 0.92 :(
## 9: 0.53125 -0.0270 35 0.55 :(
## 10: 0.59375 -0.0940 36 0.0021 **
## 11: 0.65625 -0.1600 40 0.00038 ***
## 12: 0.71875 -0.0980 36 0.0047 **
## 13: 0.78125 -0.1600 39 4.8e-05 ***
## 14: 0.84375 -0.2200 35 2.5e-06 ***
## 15: 0.90625 -0.2500 35 1.1e-06 ***
## 16: 0.96875 -0.1900 31 1.4e-05 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 33 0.046 *
## 2: 37 0.00034 ***
## 3: 38 0.00058 ***
## 4: 38 0.00028 ***
## 5: 38 0.0021 **
## 6: 34 0.031 *
## 7: 38 0.32 :(
## 8: 37 0.92 :(
## 9: 35 0.55 :(
## 10: 36 0.0021 **
## 11: 40 0.00038 ***
## 12: 36 0.0047 **
## 13: 39 4.8e-05 ***
## 14: 35 2.5e-06 ***
## 15: 35 1.1e-06 ***
## 16: 31 1.4e-05 ***
## [1] 36.2
## [1] 2.35
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.210 7 0.075 .
## 2: 0.09375 0.160 18 0.065 .
## 3: 0.15625 0.120 19 0.013 *
## 4: 0.21875 0.081 18 0.088 .
## 5: 0.28125 0.190 16 0.081 .
## 6: 0.34375 0.160 16 0.0014 **
## 7: 0.40625 0.140 19 0.13 :(
## 8: 0.46875 0.059 19 0.14 :(
## 9: 0.53125 -0.110 17 0.14 :(
## 10: 0.59375 -0.044 22 0.9 :(
## 11: 0.65625 0.010 19 0.95 :(
## 12: 0.71875 -0.069 23 0.1 :(
## 13: 0.78125 -0.130 21 0.016 *
## 14: 0.84375 -0.240 25 0.00011 ***
## 15: 0.90625 -0.200 26 0.00025 ***
## 16: 0.96875 -0.260 25 1.5e-05 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 7 0.075 .
## 2: 18 0.065 .
## 3: 19 0.013 *
## 4: 18 0.088 .
## 5: 16 0.081 .
## 6: 16 0.0014 **
## 7: 19 0.13 :(
## 8: 19 0.14 :(
## 9: 17 0.14 :(
## 10: 22 0.9 :(
## 11: 19 0.95 :(
## 12: 23 0.1 :(
## 13: 21 0.016 *
## 14: 25 0.00011 ***
## 15: 26 0.00025 ***
## 16: 25 1.5e-05 ***
## [1] 19.4
## [1] 4.59
Motor task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 -0.0440 5 0.78 :(
## 3: 0.15625 -0.0730 19 0.13 :(
## 4: 0.21875 0.0190 35 0.65 :(
## 5: 0.28125 0.0350 40 0.36 :(
## 6: 0.34375 0.0900 40 0.018 *
## 7: 0.40625 0.0540 42 0.19 :(
## 8: 0.46875 0.0560 42 0.098 .
## 9: 0.53125 0.0440 43 0.15 :(
## 10: 0.59375 -0.0100 45 0.91 :(
## 11: 0.65625 -0.0560 44 0.041 *
## 12: 0.71875 -0.0440 43 0.076 .
## 13: 0.78125 -0.0810 38 0.032 *
## 14: 0.84375 -0.1400 23 0.023 *
## 15: 0.90625 -0.0063 7 0.44 :(
## 16: 0.96875 -0.2400 4 0.2 :(
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 5 0.78 :(
## 2: 19 0.13 :(
## 3: 35 0.65 :(
## 4: 40 0.36 :(
## 5: 40 0.018 *
## 6: 42 0.19 :(
## 7: 42 0.098 .
## 8: 43 0.15 :(
## 9: 45 0.91 :(
## 10: 44 0.041 *
## 11: 43 0.076 .
## 12: 38 0.032 *
## 13: 23 0.023 *
## 14: 7 0.44 :(
## 15: 4 0.2 :(
## [1] 31.3
## [1] 15.4## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 -0.0440 5 0.78 :(
## 3: 0.15625 -0.0730 17 0.057 .
## 4: 0.21875 -0.0190 21 0.61 :(
## 5: 0.28125 0.0190 21 0.42 :(
## 6: 0.34375 0.1100 21 0.023 *
## 7: 0.40625 0.0600 20 0.16 :(
## 8: 0.46875 0.1100 20 0.024 *
## 9: 0.53125 0.1000 19 0.067 .
## 10: 0.59375 0.0880 20 0.18 :(
## 11: 0.65625 0.0051 20 1 :(
## 12: 0.71875 -0.0190 17 0.6 :(
## 13: 0.78125 -0.0560 12 0.25 :(
## 14: 0.84375 NA 0 NA
## 15: 0.90625 NA 0 NA
## 16: 0.96875 NA 0 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 5 0.78 :(
## 2: 17 0.057 .
## 3: 21 0.61 :(
## 4: 21 0.42 :(
## 5: 21 0.023 *
## 6: 20 0.16 :(
## 7: 20 0.024 *
## 8: 19 0.067 .
## 9: 20 0.18 :(
## 10: 20 1 :(
## 11: 17 0.6 :(
## 12: 12 0.25 :(
## [1] 17.8
## [1] 4.77## Warning: Removed 4 rows containing missing values (geom_point).## Warning: Removed 4 rows containing missing values (geom_errorbar).
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 0 NA
## 3: 0.15625 0.290 2 1 :(
## 4: 0.21875 0.069 14 0.29 :(
## 5: 0.28125 0.069 19 0.46 :(
## 6: 0.34375 0.076 19 0.32 :(
## 7: 0.40625 0.020 21 0.83 :(
## 8: 0.46875 -0.019 20 0.93 :(
## 9: 0.53125 0.019 20 0.69 :(
## 10: 0.59375 -0.077 20 0.076 .
## 11: 0.65625 -0.160 20 0.0074 **
## 12: 0.71875 -0.056 21 0.088 .
## 13: 0.78125 -0.081 21 0.21 :(
## 14: 0.84375 -0.160 18 0.029 *
## 15: 0.90625 -0.210 2 0.5 :(
## 16: 0.96875 NA 0 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 2 1 :(
## 2: 14 0.29 :(
## 3: 19 0.46 :(
## 4: 19 0.32 :(
## 5: 21 0.83 :(
## 6: 20 0.93 :(
## 7: 20 0.69 :(
## 8: 20 0.076 .
## 9: 20 0.0074 **
## 10: 21 0.088 .
## 11: 21 0.21 :(
## 12: 18 0.029 *
## 13: 2 0.5 :(
## [1] 16.7
## [1] 6.77## Warning: Removed 3 rows containing missing values (geom_point).## Warning: Removed 3 rows containing missing values (geom_errorbar).
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 0 NA
## 3: 0.15625 NA 0 NA
## 4: 0.21875 NA 0 NA
## 5: 0.28125 NA 0 NA
## 6: 0.34375 NA 0 NA
## 7: 0.40625 NA 1 NA
## 8: 0.46875 0.1800 2 0.5 :(
## 9: 0.53125 -0.0310 4 0.58 :(
## 10: 0.59375 -0.0270 5 0.78 :(
## 11: 0.65625 -0.0059 4 1 :(
## 12: 0.71875 -0.0520 5 0.62 :(
## 13: 0.78125 -0.0940 5 0.31 :(
## 14: 0.84375 -0.0440 5 0.59 :(
## 15: 0.90625 -0.0062 5 1 :(
## 16: 0.96875 -0.2400 4 0.2 :(
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 2 0.5 :(
## 2: 4 0.58 :(
## 3: 5 0.78 :(
## 4: 4 1 :(
## 5: 5 0.62 :(
## 6: 5 0.31 :(
## 7: 5 0.59 :(
## 8: 5 1 :(
## 9: 4 0.2 :(
## [1] 4.33
## [1] 1## Warning: Removed 7 rows containing missing values (geom_point).## Warning: Removed 7 rows containing missing values (geom_errorbar).
Sensory task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.0250 39 0.19 :(
## 2: 0.09375 0.0310 49 0.17 :(
## 3: 0.15625 0.0940 48 0.17 :(
## 4: 0.21875 0.0310 36 0.61 :(
## 5: 0.28125 0.0190 35 0.84 :(
## 6: 0.34375 -0.0190 29 0.71 :(
## 7: 0.40625 -0.0062 32 0.9 :(
## 8: 0.46875 -0.1200 32 0.038 *
## 9: 0.53125 -0.1800 29 0.0038 **
## 10: 0.59375 -0.1900 36 0.00056 ***
## 11: 0.65625 -0.1600 34 0.0016 **
## 12: 0.71875 -0.2200 35 7.2e-05 ***
## 13: 0.78125 -0.2600 34 4.7e-06 ***
## 14: 0.84375 -0.2400 41 6.8e-06 ***
## 15: 0.90625 -0.2100 49 3.9e-08 ***
## 16: 0.96875 -0.1000 52 4.3e-07 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 39 0.19 :(
## 2: 49 0.17 :(
## 3: 48 0.17 :(
## 4: 36 0.61 :(
## 5: 35 0.84 :(
## 6: 29 0.71 :(
## 7: 32 0.9 :(
## 8: 32 0.038 *
## 9: 29 0.0038 **
## 10: 36 0.00056 ***
## 11: 34 0.0016 **
## 12: 35 7.2e-05 ***
## 13: 34 4.7e-06 ***
## 14: 41 6.8e-06 ***
## 15: 49 3.9e-08 ***
## 16: 52 4.3e-07 ***
## [1] 38.1
## [1] 7.48
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.028 11 0.56 :(
## 2: 0.09375 -0.044 10 0.47 :(
## 3: 0.15625 0.094 11 0.68 :(
## 4: 0.21875 -0.085 6 0.4 :(
## 5: 0.28125 0.060 8 0.44 :(
## 6: 0.34375 -0.220 5 0.19 :(
## 7: 0.40625 -0.110 6 0.29 :(
## 8: 0.46875 -0.270 8 0.079 .
## 9: 0.53125 -0.160 5 0.44 :(
## 10: 0.59375 -0.250 8 0.058 .
## 11: 0.65625 -0.410 6 0.036 *
## 12: 0.71875 -0.470 9 0.0084 **
## 13: 0.78125 -0.310 7 0.051 .
## 14: 0.84375 -0.170 9 0.097 .
## 15: 0.90625 -0.160 9 0.043 *
## 16: 0.96875 -0.024 11 0.45 :(
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 11 0.56 :(
## 2: 10 0.47 :(
## 3: 11 0.68 :(
## 4: 6 0.4 :(
## 5: 8 0.44 :(
## 6: 5 0.19 :(
## 7: 6 0.29 :(
## 8: 8 0.079 .
## 9: 5 0.44 :(
## 10: 8 0.058 .
## 11: 6 0.036 *
## 12: 9 0.0084 **
## 13: 7 0.051 .
## 14: 9 0.097 .
## 15: 9 0.043 *
## 16: 11 0.45 :(
## [1] 8.06
## [1] 2.08
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.019 22 0.84 :(
## 2: 0.09375 0.031 24 0.57 :(
## 3: 0.15625 -0.023 21 0.58 :(
## 4: 0.21875 0.031 18 0.93 :(
## 5: 0.28125 -0.031 15 0.89 :(
## 6: 0.34375 -0.044 15 0.55 :(
## 7: 0.40625 0.018 16 1 :(
## 8: 0.46875 -0.094 16 0.22 :(
## 9: 0.53125 -0.130 16 0.04 *
## 10: 0.59375 -0.290 16 0.0047 **
## 11: 0.65625 -0.180 17 0.0099 **
## 12: 0.71875 -0.220 13 0.0077 **
## 13: 0.78125 -0.260 19 0.00034 ***
## 14: 0.84375 -0.340 19 0.00065 ***
## 15: 0.90625 -0.240 23 5.5e-05 ***
## 16: 0.96875 -0.094 24 0.00041 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 22 0.84 :(
## 2: 24 0.57 :(
## 3: 21 0.58 :(
## 4: 18 0.93 :(
## 5: 15 0.89 :(
## 6: 15 0.55 :(
## 7: 16 1 :(
## 8: 16 0.22 :(
## 9: 16 0.04 *
## 10: 16 0.0047 **
## 11: 17 0.0099 **
## 12: 13 0.0077 **
## 13: 19 0.00034 ***
## 14: 19 0.00065 ***
## 15: 23 5.5e-05 ***
## 16: 24 0.00041 ***
## [1] 18.4
## [1] 3.48
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.140 6 0.14 :(
## 2: 0.09375 0.160 15 0.056 .
## 3: 0.15625 0.130 16 0.027 *
## 4: 0.21875 0.067 12 0.29 :(
## 5: 0.28125 0.069 12 0.78 :(
## 6: 0.34375 0.065 9 0.12 :(
## 7: 0.40625 0.094 10 0.41 :(
## 8: 0.46875 -0.032 8 0.94 :(
## 9: 0.53125 -0.280 8 0.078 .
## 10: 0.59375 -0.094 12 0.45 :(
## 11: 0.65625 -0.031 11 0.69 :(
## 12: 0.71875 -0.094 13 0.26 :(
## 13: 0.78125 -0.180 8 0.056 .
## 14: 0.84375 -0.190 13 0.02 *
## 15: 0.90625 -0.210 17 0.0024 **
## 16: 0.96875 -0.160 17 0.00065 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 6 0.14 :(
## 2: 15 0.056 .
## 3: 16 0.027 *
## 4: 12 0.29 :(
## 5: 12 0.78 :(
## 6: 9 0.12 :(
## 7: 10 0.41 :(
## 8: 8 0.94 :(
## 9: 8 0.078 .
## 10: 12 0.45 :(
## 11: 11 0.69 :(
## 12: 13 0.26 :(
## 13: 8 0.056 .
## 14: 13 0.02 *
## 15: 17 0.0024 **
## 16: 17 0.00065 ***
## [1] 11.7
## [1] 3.4
Logical task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.094 36 0.0044 **
## 2: 0.09375 0.160 41 3.1e-05 ***
## 3: 0.15625 0.170 42 8.4e-05 ***
## 4: 0.21875 0.260 44 3.2e-06 ***
## 5: 0.28125 0.220 36 0.00012 ***
## 6: 0.34375 0.160 40 5.4e-05 ***
## 7: 0.40625 0.094 44 0.0061 **
## 8: 0.46875 0.031 41 0.038 *
## 9: 0.53125 -0.031 38 0.5 :(
## 10: 0.59375 -0.044 42 0.41 :(
## 11: 0.65625 -0.056 40 0.46 :(
## 12: 0.71875 -0.069 39 0.0097 **
## 13: 0.78125 -0.150 44 0.00022 ***
## 14: 0.84375 -0.230 43 2.1e-07 ***
## 15: 0.90625 -0.260 42 4.7e-07 ***
## 16: 0.96875 -0.350 27 6.1e-06 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 36 0.0044 **
## 2: 41 3.1e-05 ***
## 3: 42 8.4e-05 ***
## 4: 44 3.2e-06 ***
## 5: 36 0.00012 ***
## 6: 40 5.4e-05 ***
## 7: 44 0.0061 **
## 8: 41 0.038 *
## 9: 38 0.5 :(
## 10: 42 0.41 :(
## 11: 40 0.46 :(
## 12: 39 0.0097 **
## 13: 44 0.00022 ***
## 14: 43 2.1e-07 ***
## 15: 42 4.7e-07 ***
## 16: 27 6.1e-06 ***
## [1] 39.9
## [1] 4.3
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.150 13 0.035 *
## 2: 0.09375 0.160 13 0.025 *
## 3: 0.15625 0.130 12 0.077 .
## 4: 0.21875 0.230 12 0.016 *
## 5: 0.28125 -0.031 8 0.84 :(
## 6: 0.34375 0.160 13 0.15 :(
## 7: 0.40625 0.094 12 0.19 :(
## 8: 0.46875 0.031 11 0.16 :(
## 9: 0.53125 -0.031 11 0.22 :(
## 10: 0.59375 -0.094 11 0.16 :(
## 11: 0.65625 -0.160 7 0.2 :(
## 12: 0.71875 -0.220 9 0.011 *
## 13: 0.78125 -0.180 10 0.024 *
## 14: 0.84375 -0.340 7 0.031 *
## 15: 0.90625 -0.480 6 0.036 *
## 16: 0.96875 NA 0 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 13 0.035 *
## 2: 13 0.025 *
## 3: 12 0.077 .
## 4: 12 0.016 *
## 5: 8 0.84 :(
## 6: 13 0.15 :(
## 7: 12 0.19 :(
## 8: 11 0.16 :(
## 9: 11 0.22 :(
## 10: 11 0.16 :(
## 11: 7 0.2 :(
## 12: 9 0.011 *
## 13: 10 0.024 *
## 14: 7 0.031 *
## 15: 6 0.036 *
## [1] 10.3
## [1] 2.38## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.0520 22 0.11 :(
## 2: 0.09375 0.1800 25 0.00046 ***
## 3: 0.15625 0.2300 25 4e-04 ***
## 4: 0.21875 0.2800 24 0.00021 ***
## 5: 0.28125 0.2400 21 0.00036 ***
## 6: 0.34375 0.1800 17 0.0051 **
## 7: 0.40625 0.1200 21 0.039 *
## 8: 0.46875 0.0310 20 0.25 :(
## 9: 0.53125 0.0021 18 0.96 :(
## 10: 0.59375 -0.0260 20 0.75 :(
## 11: 0.65625 -0.1100 23 0.26 :(
## 12: 0.71875 -0.0190 20 0.42 :(
## 13: 0.78125 -0.1300 23 0.007 **
## 14: 0.84375 -0.2200 24 0.00015 ***
## 15: 0.90625 -0.2100 22 0.00081 ***
## 16: 0.96875 -0.3700 13 0.0019 **
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 22 0.11 :(
## 2: 25 0.00046 ***
## 3: 25 4e-04 ***
## 4: 24 0.00021 ***
## 5: 21 0.00036 ***
## 6: 17 0.0051 **
## 7: 21 0.039 *
## 8: 20 0.25 :(
## 9: 18 0.96 :(
## 10: 20 0.75 :(
## 11: 23 0.26 :(
## 12: 20 0.42 :(
## 13: 23 0.007 **
## 14: 24 0.00015 ***
## 15: 22 0.00081 ***
## 16: 13 0.0019 **
## [1] 21.1
## [1] 3.18
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 1 NA
## 2: 0.09375 NA 3 NA
## 3: 0.15625 0.0940 5 0.78 :(
## 4: 0.21875 0.0810 8 0.23 :(
## 5: 0.28125 0.3300 7 0.051 .
## 6: 0.34375 0.1600 10 0.0098 **
## 7: 0.40625 0.1800 11 0.17 :(
## 8: 0.46875 0.0810 10 0.31 :(
## 9: 0.53125 -0.0310 9 0.91 :(
## 10: 0.59375 0.0063 11 0.82 :(
## 11: 0.65625 0.0690 10 0.22 :(
## 12: 0.71875 -0.0690 10 0.36 :(
## 13: 0.78125 -0.0810 11 0.27 :(
## 14: 0.84375 -0.2300 12 0.0066 **
## 15: 0.90625 -0.2300 14 0.0038 **
## 16: 0.96875 -0.3400 14 0.0011 **
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 5 0.78 :(
## 2: 8 0.23 :(
## 3: 7 0.051 .
## 4: 10 0.0098 **
## 5: 11 0.17 :(
## 6: 10 0.31 :(
## 7: 9 0.91 :(
## 8: 11 0.82 :(
## 9: 10 0.22 :(
## 10: 10 0.36 :(
## 11: 11 0.27 :(
## 12: 12 0.0066 **
## 13: 14 0.0038 **
## 14: 14 0.0011 **
## [1] 10.1
## [1] 2.44## Warning: Removed 2 rows containing missing values (geom_point).## Warning: Removed 2 rows containing missing values (geom_errorbar).
Influence of Playtime on Subjective Difficulty Error
For all groups, motor, sensitive and logical
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTM)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.75587 -0.18208 0.01722 0.17996 0.67980
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07834 0.02339 3.350 0.00083 ***
## timeNorm 0.01356 0.02393 0.567 0.57104
## obj.diff -0.19206 0.03147 -6.103 1.35e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.05882497)
##
## Null deviance: 82.357 on 1362 degrees of freedom
## Residual deviance: 80.002 on 1360 degrees of freedom
## AIC: 11.387
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTS)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.81067 -0.18405 -0.03539 0.21809 0.81473
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05368 0.01828 2.936 0.00338 **
## timeNorm 0.05164 0.02428 2.127 0.03362 *
## obj.diff -0.29020 0.01884 -15.405 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06914682)
##
## Null deviance: 120.86 on 1507 degrees of freedom
## Residual deviance: 104.07 on 1505 degrees of freedom
## AIC: 255.86
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTL)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.73430 -0.20594 -0.01949 0.19850 0.71398
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.21759 0.02001 10.88 <2e-16 ***
## timeNorm 0.05914 0.02495 2.37 0.0179 *
## obj.diff -0.53045 0.02119 -25.04 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06995631)
##
## Null deviance: 156.54 on 1536 degrees of freedom
## Residual deviance: 107.31 on 1534 degrees of freedom
## AIC: 278.57
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5414894 0.5916709 -0.041797918 94 0.16 :(
## 2: 4.5 0.5347518 0.5750233 -0.031880263 141 0.16 :(
## 3: 7.5 0.5085106 0.5313589 -0.018533292 141 0.41 :(
## 4: 10.5 0.5404255 0.5341000 0.017669339 141 0.43 :(
## 5: 13.5 0.5085106 0.5167958 -0.006673180 141 0.77 :(
## 6: 16.5 0.5276596 0.5259445 0.002686940 141 0.9 :(
## 7: 19.5 0.4971631 0.5307814 -0.035626571 141 0.081 .
## 8: 22.5 0.4737589 0.4890926 -0.014471502 141 0.5 :(
## 9: 25.5 0.4758865 0.4723221 0.005367814 141 0.81 :(
## 10: 28.5 0.4574468 0.4526413 0.002528689 141 0.88 :(
## time error.diff shapes
## 1: 1.5 -0.041797918 16
## 2: 4.5 -0.031880263 16
## 3: 7.5 -0.018533292 16
## 4: 10.5 0.017669339 16
## 5: 13.5 -0.006673180 16
## 6: 16.5 0.002686940 16
## 7: 19.5 -0.035626571 16
## 8: 22.5 -0.014471502 16
## 9: 25.5 0.005367814 16
## 10: 28.5 0.002528689 16
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4682692 0.5971846 -0.13701885 104 2.8e-05 ***
## 2: 4.5 0.5076923 0.6238752 -0.09911341 156 1.4e-07 ***
## 3: 7.5 0.4660256 0.5314762 -0.06766846 156 0.0022 **
## 4: 10.5 0.5160256 0.5982780 -0.07895358 156 8.9e-05 ***
## 5: 13.5 0.4679487 0.5755768 -0.09400514 156 2.4e-07 ***
## 6: 16.5 0.4211538 0.5249293 -0.10790217 156 2.1e-06 ***
## 7: 19.5 0.4794872 0.5492939 -0.05610758 156 0.001 **
## 8: 22.5 0.4993590 0.5710919 -0.05957949 156 0.0025 **
## 9: 25.5 0.5474359 0.5949924 -0.03147437 156 0.069 .
## 10: 28.5 0.4993590 0.5713447 -0.06615746 156 0.0014 **
## time error.diff shapes
## 1: 1.5 -0.13701885 24
## 2: 4.5 -0.09911341 24
## 3: 7.5 -0.06766846 24
## 4: 10.5 -0.07895358 24
## 5: 13.5 -0.09400514 24
## 6: 16.5 -0.10790217 24
## 7: 19.5 -0.05610758 24
## 8: 22.5 -0.05957949 24
## 9: 25.5 -0.03147437 16
## 10: 28.5 -0.06615746 24
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4415094 0.6007697 -1.658770e-01 106 3.8e-06 ***
## 2: 4.5 0.5119497 0.6324837 -1.343840e-01 159 4.2e-06 ***
## 3: 7.5 0.5100629 0.5479813 -4.895619e-02 159 0.069 .
## 4: 10.5 0.5220126 0.5177334 2.196993e-03 159 0.93 :(
## 5: 13.5 0.5169811 0.5303606 -2.035258e-02 159 0.43 :(
## 6: 16.5 0.5100629 0.5026471 2.226322e-05 159 1 :(
## 7: 19.5 0.4584906 0.4514766 -3.401739e-03 159 0.87 :(
## 8: 22.5 0.4226415 0.4287566 -1.335901e-02 159 0.6 :(
## 9: 25.5 0.4584906 0.3964332 6.936761e-02 159 0.013 *
## 10: 28.5 0.4446541 0.3652666 6.326623e-02 159 0.012 *
## time error.diff shapes
## 1: 1.5 -1.658770e-01 24
## 2: 4.5 -1.343840e-01 24
## 3: 7.5 -4.895619e-02 16
## 4: 10.5 2.196993e-03 16
## 5: 13.5 -2.035258e-02 16
## 6: 16.5 2.226322e-05 16
## 7: 19.5 -3.401739e-03 16
## 8: 22.5 -1.335901e-02 16
## 9: 25.5 6.936761e-02 24
## 10: 28.5 6.326623e-02 24
For all taks, per group
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTAll[niveau.group == "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.79883 -0.21505 -0.01729 0.22881 0.62303
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.20813 0.02735 7.611 6.07e-14 ***
## timeNorm 0.10063 0.02924 3.441 0.000602 ***
## obj.diff -0.50467 0.02773 -18.199 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06870327)
##
## Null deviance: 96.236 on 1043 degrees of freedom
## Residual deviance: 71.520 on 1041 degrees of freedom
## AIC: 171.95
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTAll[niveau.group == "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.75152 -0.20645 -0.01803 0.22635 0.77747
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.14649 0.01751 8.367 <2e-16 ***
## timeNorm 0.04591 0.02155 2.131 0.0332 *
## obj.diff -0.39517 0.01981 -19.944 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07295733)
##
## Null deviance: 180.79 on 2058 degrees of freedom
## Residual deviance: 150.00 on 2056 degrees of freedom
## AIC: 457.97
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTAll[niveau.group == "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.7594 -0.1583 -0.0194 0.1839 0.7065
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.09344 0.01913 4.883 1.17e-06 ***
## timeNorm 0.02052 0.02444 0.840 0.401
## obj.diff -0.25866 0.02502 -10.338 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.05859095)
##
## Null deviance: 82.998 on 1304 degrees of freedom
## Residual deviance: 76.285 on 1302 degrees of freedom
## AIC: 5.9127
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5236111 0.7369714 -0.22729241 72 1.5e-07 ***
## 2: 4.5 0.5990741 0.7689461 -0.19665418 108 3.4e-07 ***
## 3: 7.5 0.6009259 0.7074381 -0.11841858 108 0.00037 ***
## 4: 10.5 0.6435185 0.7036733 -0.05775307 108 0.05 .
## 5: 13.5 0.6166667 0.7307579 -0.12837600 108 3.4e-05 ***
## 6: 16.5 0.6064815 0.6727884 -0.07668353 108 0.017 *
## 7: 19.5 0.6074074 0.6754176 -0.06695520 108 0.01 *
## 8: 22.5 0.6324074 0.7093334 -0.08254905 108 0.022 *
## 9: 25.5 0.6055556 0.6569905 -0.04087682 108 0.2 :(
## 10: 28.5 0.6194444 0.6461943 -0.01537350 108 0.58 :(
## time error.diff shapes
## 1: 1.5 -0.22729241 24
## 2: 4.5 -0.19665418 24
## 3: 7.5 -0.11841858 24
## 4: 10.5 -0.05775307 16
## 5: 13.5 -0.12837600 24
## 6: 16.5 -0.07668353 24
## 7: 19.5 -0.06695520 24
## 8: 22.5 -0.08254905 24
## 9: 25.5 -0.04087682 16
## 10: 28.5 -0.01537350 16
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4767606 0.5680143 -0.0913854061 142 0.0016 **
## 2: 4.5 0.5164319 0.6238517 -0.1014397363 213 3.2e-07 ***
## 3: 7.5 0.4877934 0.5122493 -0.0296815499 213 0.16 :(
## 4: 10.5 0.5159624 0.5538451 -0.0374382444 213 0.06 .
## 5: 13.5 0.5145540 0.5319721 -0.0174015871 213 0.38 :(
## 6: 16.5 0.4732394 0.5211852 -0.0502809133 213 0.0081 **
## 7: 19.5 0.4798122 0.5159316 -0.0388561400 213 0.037 *
## 8: 22.5 0.4154930 0.4524753 -0.0441414501 213 0.022 *
## 9: 25.5 0.4985915 0.4798915 0.0120783775 213 0.53 :(
## 10: 28.5 0.4751174 0.4595756 -0.0006611057 213 0.98 :(
## time error.diff shapes
## 1: 1.5 -0.0913854061 24
## 2: 4.5 -0.1014397363 24
## 3: 7.5 -0.0296815499 16
## 4: 10.5 -0.0374382444 16
## 5: 13.5 -0.0174015871 16
## 6: 16.5 -0.0502809133 24
## 7: 19.5 -0.0388561400 24
## 8: 22.5 -0.0441414501 24
## 9: 25.5 0.0120783775 16
## 10: 28.5 -0.0006611057 16
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4555556 0.5298432 -0.057470313 90 0.051 .
## 2: 4.5 0.4540741 0.4669715 -0.013612699 135 0.54 :(
## 3: 7.5 0.4200000 0.4403593 -0.017158731 135 0.43 :(
## 4: 10.5 0.4466667 0.4221730 0.029500139 135 0.19 :(
## 5: 13.5 0.3755556 0.4055824 -0.028420704 135 0.22 :(
## 6: 16.5 0.4066667 0.3873663 0.014590519 135 0.49 :(
## 7: 19.5 0.3703704 0.3664908 -0.004719346 135 0.84 :(
## 8: 22.5 0.4081481 0.3943664 0.010617565 135 0.57 :(
## 9: 25.5 0.3985185 0.3650166 0.029284168 135 0.14 :(
## 10: 28.5 0.3333333 0.3211185 0.004909218 135 0.78 :(
## time error.diff shapes
## 1: 1.5 -0.057470313 16
## 2: 4.5 -0.013612699 16
## 3: 7.5 -0.017158731 16
## 4: 10.5 0.029500139 16
## 5: 13.5 -0.028420704 16
## 6: 16.5 0.014590519 16
## 7: 19.5 -0.004719346 16
## 8: 22.5 0.010617565 16
## 9: 25.5 0.029284168 16
## 10: 28.5 0.004909218 16
Per group, motor task
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTM[niveau.group == "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.72641 -0.18104 0.07896 0.17901 0.32506
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.17441 0.11392 1.531 0.1280
## timeNorm 0.05973 0.06311 0.946 0.3455
## obj.diff -0.34358 0.13212 -2.600 0.0103 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.04409487)
##
## Null deviance: 6.6352 on 144 degrees of freedom
## Residual deviance: 6.2615 on 142 degrees of freedom
## AIC: -36.144
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.7000000 0.8422160 -0.1313282577 10 0.084 .
## 2: 4.5 0.7200000 0.8045511 -0.0795853843 15 0.49 :(
## 3: 7.5 0.6933333 0.7637930 -0.0692528767 15 0.25 :(
## 4: 10.5 0.7200000 0.7894410 -0.0625540262 15 0.36 :(
## 5: 13.5 0.7000000 0.8006171 -0.1084499111 15 0.055 .
## 6: 16.5 0.7200000 0.7661172 -0.0140493196 15 0.8 :(
## 7: 19.5 0.7466667 0.7396280 0.0120888681 15 0.8 :(
## 8: 22.5 0.7333333 0.7489324 -0.0006995685 15 1 :(
## 9: 25.5 0.7533333 0.8163298 -0.0314486706 15 0.6 :(
## 10: 28.5 0.6866667 0.7440259 -0.0101905199 15 0.85 :(
## time error.diff shapes
## 1: 1.5 -0.1313282577 16
## 2: 4.5 -0.0795853843 16
## 3: 7.5 -0.0692528767 16
## 4: 10.5 -0.0625540262 16
## 5: 13.5 -0.1084499111 16
## 6: 16.5 -0.0140493196 16
## 7: 19.5 0.0120888681 16
## 8: 22.5 -0.0006995685 16
## 9: 25.5 -0.0314486706 16
## 10: 28.5 -0.0101905199 16
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTM[niveau.group == "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.7008 -0.1756 0.0104 0.2007 0.6764
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.129171 0.042821 3.017 0.00266 **
## timeNorm -0.001606 0.039220 -0.041 0.96736
## obj.diff -0.312573 0.058219 -5.369 1.13e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06994394)
##
## Null deviance: 44.480 on 608 degrees of freedom
## Residual deviance: 42.386 on 606 degrees of freedom
## AIC: 113.28
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5214286 0.6226413 -0.094272559 42 0.054 .
## 2: 4.5 0.5476190 0.6192837 -0.065077266 63 0.076 .
## 3: 7.5 0.5222222 0.5488374 -0.021946708 63 0.54 :(
## 4: 10.5 0.5269841 0.5633358 -0.017849848 63 0.63 :(
## 5: 13.5 0.5365079 0.5457213 -0.003702683 63 0.93 :(
## 6: 16.5 0.5285714 0.5497442 -0.024443456 63 0.5 :(
## 7: 19.5 0.4698413 0.5571074 -0.092288817 63 0.0066 **
## 8: 22.5 0.4412698 0.5026156 -0.066421840 63 0.067 .
## 9: 25.5 0.4777778 0.4906858 -0.015672991 63 0.67 :(
## 10: 28.5 0.4777778 0.4965908 -0.024207547 63 0.42 :(
## time error.diff shapes
## 1: 1.5 -0.094272559 16
## 2: 4.5 -0.065077266 16
## 3: 7.5 -0.021946708 16
## 4: 10.5 -0.017849848 16
## 5: 13.5 -0.003702683 16
## 6: 16.5 -0.024443456 16
## 7: 19.5 -0.092288817 24
## 8: 22.5 -0.066421840 16
## 9: 25.5 -0.015672991 16
## 10: 28.5 -0.024207547 16
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTM[niveau.group == "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.66104 -0.16469 -0.00053 0.17110 0.56752
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.003495 0.031165 0.112 0.911
## timeNorm 0.030048 0.032813 0.916 0.360
## obj.diff 0.014011 0.048027 0.292 0.771
##
## (Dispersion parameter for gaussian family taken to be 0.04854566)
##
## Null deviance: 29.460 on 608 degrees of freedom
## Residual deviance: 29.419 on 606 degrees of freedom
## AIC: -109.12
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5238095 0.5010468 0.029289623 42 0.44 :(
## 2: 4.5 0.4777778 0.4761135 0.006623743 63 0.82 :(
## 3: 7.5 0.4507937 0.4585390 -0.004803129 63 0.9 :(
## 4: 10.5 0.5111111 0.4440686 0.079755086 63 0.014 *
## 5: 13.5 0.4349206 0.4202938 0.019074305 63 0.57 :(
## 6: 16.5 0.4809524 0.4449609 0.036542116 63 0.21 :(
## 7: 19.5 0.4650794 0.4547299 0.003697701 63 0.89 :(
## 8: 22.5 0.4444444 0.4137030 0.029021771 63 0.27 :(
## 9: 25.5 0.4079365 0.3720518 0.034615081 63 0.19 :(
## 10: 28.5 0.3825397 0.3393145 0.035297116 63 0.18 :(
## time error.diff shapes
## 1: 1.5 0.029289623 16
## 2: 4.5 0.006623743 16
## 3: 7.5 -0.004803129 16
## 4: 10.5 0.079755086 24
## 5: 13.5 0.019074305 16
## 6: 16.5 0.036542116 16
## 7: 19.5 0.003697701 16
## 8: 22.5 0.029021771 16
## 9: 25.5 0.034615081 16
## 10: 28.5 0.035297116 16
Per group, sensory task
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTS[niveau.group == "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.80551 -0.24498 0.03447 0.22130 0.66238
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.16147 0.03394 4.757 2.59e-06 ***
## timeNorm 0.06204 0.04210 1.474 0.141
## obj.diff -0.41368 0.03423 -12.086 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06791038)
##
## Null deviance: 43.406 on 492 degrees of freedom
## Residual deviance: 33.276 on 490 degrees of freedom
## AIC: 78.108
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4941176 0.6079001 -0.152223903 34 0.024 *
## 2: 4.5 0.5901961 0.6984424 -0.109220667 51 0.014 *
## 3: 7.5 0.5509804 0.6337725 -0.086832289 51 0.051 .
## 4: 10.5 0.6294118 0.6829108 -0.044689512 51 0.2 :(
## 5: 13.5 0.5862745 0.7004956 -0.102981538 51 0.0039 **
## 6: 16.5 0.4862745 0.5635022 -0.101321899 51 0.053 .
## 7: 19.5 0.5549020 0.6149413 -0.063019127 51 0.13 :(
## 8: 22.5 0.6823529 0.6970945 -0.007760103 51 0.91 :(
## 9: 25.5 0.5607843 0.6073046 -0.038560219 51 0.36 :(
## 10: 28.5 0.5647059 0.6293812 -0.043298474 51 0.25 :(
## time error.diff shapes
## 1: 1.5 -0.152223903 24
## 2: 4.5 -0.109220667 24
## 3: 7.5 -0.086832289 16
## 4: 10.5 -0.044689512 16
## 5: 13.5 -0.102981538 24
## 6: 16.5 -0.101321899 16
## 7: 19.5 -0.063019127 16
## 8: 22.5 -0.007760103 16
## 9: 25.5 -0.038560219 16
## 10: 28.5 -0.043298474 16
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTS[niveau.group == "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.7638 -0.1639 -0.0337 0.2151 0.8308
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.02975 0.02706 1.099 0.272
## timeNorm 0.05704 0.03585 1.591 0.112
## obj.diff -0.26768 0.02795 -9.578 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06953452)
##
## Null deviance: 54.799 on 695 degrees of freedom
## Residual deviance: 48.187 on 693 degrees of freedom
## AIC: 124.67
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4812500 0.6044755 -0.12338343 48 0.012 *
## 2: 4.5 0.5055556 0.6338991 -0.09774641 72 1.9e-05 ***
## 3: 7.5 0.4319444 0.4848271 -0.06463323 72 0.058 .
## 4: 10.5 0.5000000 0.6264097 -0.11767551 72 0.00013 ***
## 5: 13.5 0.4527778 0.5417625 -0.07409688 72 0.003 **
## 6: 16.5 0.4250000 0.5596252 -0.12236380 72 0.00013 ***
## 7: 19.5 0.5000000 0.5621298 -0.03398795 72 0.17 :(
## 8: 22.5 0.3819444 0.5013876 -0.10892654 72 0.00013 ***
## 9: 25.5 0.5666667 0.6095434 -0.02536096 72 0.25 :(
## 10: 28.5 0.5152778 0.5645340 -0.06359278 72 0.038 *
## time error.diff shapes
## 1: 1.5 -0.12338343 24
## 2: 4.5 -0.09774641 24
## 3: 7.5 -0.06463323 16
## 4: 10.5 -0.11767551 24
## 5: 13.5 -0.07409688 24
## 6: 16.5 -0.12236380 24
## 7: 19.5 -0.03398795 16
## 8: 22.5 -0.10892654 24
## 9: 25.5 -0.02536096 16
## 10: 28.5 -0.06359278 24
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTS[niveau.group == "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.70089 -0.14161 -0.03443 0.22294 0.78660
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.004861 0.035681 0.136 0.892
## timeNorm 0.018261 0.051095 0.357 0.721
## obj.diff -0.221535 0.039223 -5.648 3.62e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06475505)
##
## Null deviance: 22.533 on 318 degrees of freedom
## Residual deviance: 20.463 on 316 degrees of freedom
## AIC: 37.12
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4000000 0.5647170 -0.15559095 22 0.01 *
## 2: 4.5 0.3848485 0.4867648 -0.08569054 33 0.031 *
## 3: 7.5 0.4090909 0.4751618 -0.04598467 33 0.15 :(
## 4: 10.5 0.3757576 0.4061035 -0.04026060 33 0.22 :(
## 5: 13.5 0.3181818 0.4562970 -0.13311540 33 0.0035 **
## 6: 16.5 0.3121212 0.3896165 -0.08688704 33 0.018 *
## 7: 19.5 0.3181818 0.4198331 -0.08121115 33 0.0052 **
## 8: 22.5 0.4727273 0.5284428 -0.02323492 33 0.54 :(
## 9: 25.5 0.4848485 0.5442167 -0.03182923 33 0.32 :(
## 10: 28.5 0.3636364 0.4965117 -0.10661909 33 0.0043 **
## time error.diff shapes
## 1: 1.5 -0.15559095 24
## 2: 4.5 -0.08569054 24
## 3: 7.5 -0.04598467 16
## 4: 10.5 -0.04026060 16
## 5: 13.5 -0.13311540 24
## 6: 16.5 -0.08688704 24
## 7: 19.5 -0.08121115 24
## 8: 22.5 -0.02323492 16
## 9: 25.5 -0.03182923 16
## 10: 28.5 -0.10661909 24
Per group, logical task
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTL[niveau.group == "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.7180 -0.1523 -0.0701 0.2646 0.5316
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.36438 0.05390 6.761 4.83e-11 ***
## timeNorm 0.11540 0.04908 2.351 0.0192 *
## obj.diff -0.74698 0.05334 -14.004 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07166686)
##
## Null deviance: 45.424 on 405 degrees of freedom
## Residual deviance: 28.882 on 403 degrees of freedom
## AIC: 87.062
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4964286 0.8561134 -0.36716892 28 7.5e-08 ***
## 2: 4.5 0.5666667 0.8418417 -0.29004109 42 9e-07 ***
## 3: 7.5 0.6285714 0.7767624 -0.16850164 42 0.0071 **
## 4: 10.5 0.6333333 0.6982535 -0.07050893 42 0.21 :(
## 5: 13.5 0.6238095 0.7425551 -0.16270974 42 0.023 *
## 6: 16.5 0.7119048 0.7721614 -0.07008744 42 0.23 :(
## 7: 19.5 0.6214286 0.7259209 -0.11110926 42 0.023 *
## 8: 22.5 0.5357143 0.7100524 -0.19151750 42 0.0023 **
## 9: 25.5 0.6071429 0.6604165 -0.04212140 42 0.55 :(
## 10: 28.5 0.6619048 0.6316703 0.02540027 42 0.62 :(
## time error.diff shapes
## 1: 1.5 -0.36716892 24
## 2: 4.5 -0.29004109 24
## 3: 7.5 -0.16850164 24
## 4: 10.5 -0.07050893 16
## 5: 13.5 -0.16270974 24
## 6: 16.5 -0.07008744 16
## 7: 19.5 -0.11110926 24
## 8: 22.5 -0.19151750 24
## 9: 25.5 -0.04212140 16
## 10: 28.5 0.02540027 16## Warning: Removed 2 rows containing missing values (geom_errorbar).
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTL[niveau.group == "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.64743 -0.21112 -0.01143 0.18810 0.69300
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.24374 0.02866 8.503 <2e-16 ***
## timeNorm 0.04799 0.03619 1.326 0.185
## obj.diff -0.53789 0.03229 -16.657 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07228863)
##
## Null deviance: 76.556 on 753 degrees of freedom
## Residual deviance: 54.289 on 751 degrees of freedom
## AIC: 163.93
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4365385 0.4902360 -0.056484920 52 0.23 :(
## 2: 4.5 0.5012821 0.6182667 -0.127211343 78 0.0017 **
## 3: 7.5 0.5115385 0.5080102 -0.009983459 78 0.85 :(
## 4: 10.5 0.5217949 0.4791968 0.031134779 78 0.36 :(
## 5: 13.5 0.5538462 0.5118296 0.042413783 78 0.27 :(
## 6: 16.5 0.4730769 0.4626353 -0.001909564 78 0.96 :(
## 7: 19.5 0.4692308 0.4400298 0.018917033 78 0.6 :(
## 8: 22.5 0.4256410 0.3668275 0.061870825 78 0.2 :(
## 9: 25.5 0.4525641 0.3514944 0.103712580 78 0.0095 **
## 10: 28.5 0.4358974 0.3327941 0.093319755 78 0.026 *
## time error.diff shapes
## 1: 1.5 -0.056484920 16
## 2: 4.5 -0.127211343 24
## 3: 7.5 -0.009983459 16
## 4: 10.5 0.031134779 16
## 5: 13.5 0.042413783 16
## 6: 16.5 -0.001909564 16
## 7: 19.5 0.018917033 16
## 8: 22.5 0.061870825 16
## 9: 25.5 0.103712580 24
## 10: 28.5 0.093319755 24
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTL[niveau.group == "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.59428 -0.20393 0.00244 0.19029 0.63266
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.20091 0.03687 5.449 9.2e-08 ***
## timeNorm -0.02360 0.04793 -0.492 0.623
## obj.diff -0.50212 0.04973 -10.097 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.05609687)
##
## Null deviance: 27.59 on 376 degrees of freedom
## Residual deviance: 20.98 on 374 degrees of freedom
## AIC: -11.147
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3923077 0.5468516 -0.14292222 26 0.043 *
## 2: 4.5 0.4743590 0.4354555 0.03036380 39 0.64 :(
## 3: 7.5 0.3794872 0.3815438 -0.01099869 39 0.79 :(
## 4: 10.5 0.4025641 0.4004002 0.01597914 39 0.85 :(
## 5: 13.5 0.3282051 0.3389055 -0.01793324 39 0.63 :(
## 6: 16.5 0.3666667 0.2924247 0.07494671 39 0.11 :(
## 7: 19.5 0.2615385 0.1788149 0.07887150 39 0.14 :(
## 8: 22.5 0.2948718 0.2496809 0.01582368 39 0.58 :(
## 9: 25.5 0.3102564 0.2020211 0.11168377 39 0.06 .
## 10: 28.5 0.2282051 0.1433153 0.06983217 39 0.26 :(
## time error.diff shapes
## 1: 1.5 -0.14292222 24
## 2: 4.5 0.03036380 16
## 3: 7.5 -0.01099869 16
## 4: 10.5 0.01597914 16
## 5: 13.5 -0.01793324 16
## 6: 16.5 0.07494671 16
## 7: 19.5 0.07887150 16
## 8: 22.5 0.01582368 16
## 9: 25.5 0.11168377 16
## 10: 28.5 0.06983217 16
Link between d(t-1) and error
##
## Call:
## glm(formula = error.subj.diff.confiance ~ est.confidence.norm,
## data = DTM)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.80752 -0.18194 0.01673 0.18203 0.75844
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.02042 0.01368 1.492 0.13585
## est.confidence.norm -0.07064 0.02440 -2.895 0.00385 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06014176)
##
## Null deviance: 82.357 on 1362 degrees of freedom
## Residual deviance: 81.853 on 1361 degrees of freedom
## AIC: 40.563
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ est.confidence.norm,
## data = DTS)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.90536 -0.17200 0.03079 0.12460 0.97153
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.07759 0.01437 -5.400 7.72e-08 ***
## est.confidence.norm -0.01530 0.02531 -0.604 0.546
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.08023266)
##
## Null deviance: 120.86 on 1507 degrees of freedom
## Residual deviance: 120.83 on 1506 degrees of freedom
## AIC: 479.1
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ est.confidence.norm,
## data = DTL)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.95702 -0.21545 -0.02456 0.22257 0.95697
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05462 0.01698 3.217 0.00132 **
## est.confidence.norm -0.12831 0.02840 -4.518 6.72e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1006412)
##
## Null deviance: 156.54 on 1536 degrees of freedom
## Residual deviance: 154.48 on 1535 degrees of freedom
## AIC: 836.57
##
## Number of Fisher Scoring iterations: 2## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTAll
##
## REML criterion at convergence: 1118.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4812 -0.6466 0.0062 0.6060 3.4107
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.009563 0.09779
## Residual 0.073039 0.27026
## Number of obs: 4408, groups: IDjoueur, 55
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.02069 0.01614 97.00000 -1.282 0.2030
## est.confidence.norm -0.04165 0.01642 4274.00000 -2.536 0.0112 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.514
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTM
##
## REML criterion at convergence: -517.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0052 -0.6756 -0.0384 0.6900 3.1371
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.02495 0.1579
## Residual 0.03582 0.1893
## Number of obs: 1363, groups: IDjoueur, 47
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.668e-03 2.757e-02 8.110e+01 0.097 0.923
## est.confidence.norm -3.443e-02 2.905e-02 1.313e+03 -1.185 0.236
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.517
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTS
##
## REML criterion at convergence: 355.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1459 -0.6551 0.0612 0.5685 3.7935
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.0115 0.1072
## Residual 0.0693 0.2632
## Number of obs: 1508, groups: IDjoueur, 52
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.10785 0.02277 133.90000 -4.737 5.45e-06 ***
## est.confidence.norm 0.04658 0.03241 869.00000 1.437 0.151
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.696
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTL
##
## REML criterion at convergence: 685.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9615 -0.6605 -0.0590 0.6527 3.2759
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01927 0.1388
## Residual 0.08483 0.2913
## Number of obs: 1537, groups: IDjoueur, 53
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.04730 0.02869 130.10000 -1.649 0.1016
## est.confidence.norm 0.06558 0.03824 912.70000 1.715 0.0867 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.701## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTAll
##
## REML criterion at convergence: 1105.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4322 -0.6297 0.0101 0.6145 3.3779
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.009928 0.09964
## Residual 0.072839 0.26989
## Number of obs: 4408, groups: IDjoueur, 55
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.11565 0.04114 2289.00000 2.811 0.00498 **
## est.confidence.norm -0.31480 0.07731 4361.00000 -4.072 4.75e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.940
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTM
##
## REML criterion at convergence: -530.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9017 -0.6791 -0.0238 0.7003 3.1515
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.02531 0.1591
## Residual 0.03551 0.1884
## Number of obs: 1363, groups: IDjoueur, 47
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.13790 0.04926 612.50000 2.799 0.005283 **
## est.confidence.norm -0.30413 0.08628 1318.10000 -3.525 0.000438 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.876
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTS
##
## REML criterion at convergence: 349.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1716 -0.6602 0.0646 0.5649 3.8186
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01094 0.1046
## Residual 0.06927 0.2632
## Number of obs: 1508, groups: IDjoueur, 52
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.07851 0.07601 1494.20000 1.033 0.3018
## est.confidence.norm -0.32688 0.14848 1470.70000 -2.202 0.0279 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.978
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTL
##
## REML criterion at convergence: 676.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9927 -0.6613 -0.0566 0.6609 3.2008
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01737 0.1318
## Residual 0.08471 0.2910
## Number of obs: 1537, groups: IDjoueur, 53
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.2073 0.0742 1469.8000 2.794 0.00528 **
## est.confidence.norm -0.4398 0.1430 1490.4000 -3.075 0.00214 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.965Subjective difficulty and play habits
Playing video game in general and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -2.0243, p-value = 0.04294
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.1276282
##
## [1] "pvg.on.error -0.13 0.043 *"Playing board game in general and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -2.4891, p-value = 0.01281
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.1547884
##
## [1] "pbg.on.error -0.15 0.013 *"In game level and subjective difficulty error
## [1] "niveau.group.on.error.l 0.092 ."
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.5696, p-value = 0.1165
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.08637111
##
## Kendall's rank correlation tau
##
## data: Y and X
## T = 523, p-value = 0.7565
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.03237743
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.0258, p-value = 0.305
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.09803922
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.8563, p-value = 0.06341
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1756168
##
## [1] "niveau.group.on.error.l 0.18 0.063 ."Sex and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 3.3506, p-value = 0.0008062
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2247349
##
## [1] "sexe.on.error 0.22 0.00081 ***"## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.6384, p-value = 0.1013
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1993259
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.1555, p-value = 0.03112
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2488067
##
## [1] "sexe.on.error.s 0.25 0.031 *"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.966, p-value = 0.0493
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2246958
##
## [1] "sexe.on.error.l 0.22 0.049 *"
##
## Wilcoxon rank sum test with continuity correction
##
## data: B and A
## W = 3490, p-value = 0.0008119
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.01773350 0.09107901
## sample estimates:
## difference in location
## 0.05579481
##
## [1] "sexe.on.error.2 0.056 0.00081 *** mean(A): -0.057 mean(B): -0.0023"
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 329, p-value = 0.1041
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.006855887 0.109293413
## sample estimates:
## difference in location
## 0.04767336
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 408, p-value = 0.0309
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.005635925 0.122074625
## sample estimates:
## difference in location
## 0.06400045
##
## [1] "sexe.on.error.s.2 0.064 0.031 * mean(A): -0.059 mean(B): 0.004"
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 429, p-value = 0.04975
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.0002747141 0.1116709810
## sample estimates:
## difference in location
## 0.05413278
##
## [1] "sexe.on.error.l.2 0.054 0.05 . mean(A): -0.06 mean(B): -0.0073"Risk aversion and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.52339, p-value = 0.6007
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.03130149## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.39687, p-value = 0.6915
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.04335873
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.72909, p-value = 0.4659
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.07499906
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.4961, p-value = 0.6198
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.05055382Self efficacy and subjective difficulty error
## Warning: Removed 74 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -3.4234, p-value = 0.0006184
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2763483
##
## [1] "self.eff.on.error -0.28 0.00062 ***"## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 23 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.9464, p-value = 0.05161
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2900611
##
## [1] "self.eff.on.error -0.29 0.052 ."## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 24 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.8079, p-value = 0.07063
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2470292
##
## [1] "self.eff.on.error -0.25 0.071 ."## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 27 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -2.0636, p-value = 0.03905
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2944232
##
## [1] "self.eff.on.error -0.29 0.039 *"